Spatial graphlet matching kernel for recognizing aerial image categories

Zhang, L; Song, M; Sun, L; Liu, X; Wang, Y; Tao, D; Bu, J; Chen, C

Spatial graphlet matching kernel for recognizing aerial image categories

Zhang, L Song, M Sun, L Liu, X Wang, Y Tao, D

Bu, J Chen, C

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Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Pattern Recognition, 2012, pp. 2813 - 2816
Issue Date:: 2012-01-01

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Field	Value	Language
dc.contributor.author	Zhang, L	en_US
dc.contributor.author	Song, M	en_US
dc.contributor.author	Sun, L	en_US
dc.contributor.author	Liu, X	en_US
dc.contributor.author	Wang, Y	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Bu, J	en_US
dc.contributor.author	Chen, C	en_US
dc.date.issued	2012-01-01	en_US
dc.identifier.citation	Proceedings - International Conference on Pattern Recognition, 2012, pp. 2813 - 2816	en_US
dc.identifier.isbn	9784990644109	en_US
dc.identifier.issn	1051-4651	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119895
dc.description.abstract	This paper presents a method for recognizing aerial image categories based on matching graphlets(i.e., small connected subgraphs) extracted from aerial images. By constructing a Region Adjacency Graph (RAG) to encode the geometric property and the color distribution of each aerial image, we cast aerial image category recognition as RAG-to-RAG matching. Based on graph theory, RAG-to-RAG matching is conducted by matching all their respective graphlets. Towards an effective graphlet matching process, we develop a manifold embedding algorithm to transfer different-sized graphlets into equal length feature vectors and further integrate these feature vectors into a kernel. This kernel is used to train a SVM [8] classifier for aerial image categories recognition. Experimental results demonstrate our method outperforms several state-of-the-art object/scene recognition models. © 2012 ICPR Org Committee.	en_US
dc.relation.ispartof	Proceedings - International Conference on Pattern Recognition	en_US
dc.title	Spatial graphlet matching kernel for recognizing aerial image categories	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

This paper presents a method for recognizing aerial image categories based on matching graphlets(i.e., small connected subgraphs) extracted from aerial images. By constructing a Region Adjacency Graph (RAG) to encode the geometric property and the color distribution of each aerial image, we cast aerial image category recognition as RAG-to-RAG matching. Based on graph theory, RAG-to-RAG matching is conducted by matching all their respective graphlets. Towards an effective graphlet matching process, we develop a manifold embedding algorithm to transfer different-sized graphlets into equal length feature vectors and further integrate these feature vectors into a kernel. This kernel is used to train a SVM [8] classifier for aerial image categories recognition. Experimental results demonstrate our method outperforms several state-of-the-art object/scene recognition models. © 2012 ICPR Org Committee.

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http://hdl.handle.net/10453/119895